TY  - BOOK
AU  - Hastie,Trevor
AU  - Tibshirani,Robert
AU  - Friedman,J.H.
TI  - Elements of statistical learning: data mining, inference, and prediction 
T2  - Springer series in statistics,
SN  - 9780387848570
U1  - 519.5 
PY  - 2009///
CY  - New York
PB  - Springer
KW  - Supervised learning (Machine learning)
KW  - Electronic data processing
KW  - Statistics
KW  - Biology
KW  - Data processing
KW  - Computational biology
KW  - Mathematics
KW  - Data mining
KW  - Computer Science
KW  - IT
KW  - COMPUTERS
KW  - Database Management
KW  - Data Mining
KW  - bisacsh
KW  - cct
KW  - fast
KW  - Machine-learning
KW  - gtt
KW  - Datamining
KW  - Prognoses
KW  - Estatistica computacional
KW  - larpcal
KW  - Estatistica
KW  - Mineracao de dados
KW  - Inferencia estatistica
KW  - Maschinelles Lernen
KW  - swd
KW  - Statistik
KW  - gnd
KW  - Statistics as Topic
KW  - Computational Biology
KW  - Mathematical Computing
KW  - Electronic books
N1  - Second edition corrected at 7th printing in 2013; Includes bibliographical references (pages 699-727) and indexes; 1; Introduction --; 2; Overview of supervised learning --; 3; Linear methods for regression --; 4; Linear methods for classification --; 5; Basis expansions and regularization --; 6; Kernel smoothing methods --; 7; Model assessment and selection --; 8; Model inference and averaging --; 9; Additive models, trees, and related methods --; 10; Boosting and additive trees --; 11; Neural networks --; 12; Support vector machines and flexible discriminants --; 13; Prototype methods and nearest-neighbors --; 14; Unsupervised learning --; 15; Random forests --; 16; Ensemble learning --; 17; Undirected graphical models --; 18; High-dimensional problems: p>> N
N2  - "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."--Jacket
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